code: def process_pandas(data, endog_idx=0, exog_idx=None, index_idx=None):
      names = data.columns
  
      if isinstance(endog_idx, int):
          endog_name = names[endog_idx]
          endog = data[endog_name].copy()
          if exog_idx is None:
              exog = data.drop([endog_name], axis=1)
          else:
              exog = data[names[exog_idx]].copy()
      else:
          endog = data.loc[:, endog_idx].copy()
          endog_name = list(endog.columns)
          if exog_idx is None:
              exog = data.drop(endog_name, axis=1)
          elif isinstance(exog_idx, int):
              exog = data[names[exog_idx]].copy()
          else:
              exog = data[names[exog_idx]].copy()
  
      if (
          index_idx is not None
      ):  # NOTE: will have to be improved for dates
          index = Index(data.iloc[:, index_idx])
          endog.index = index
          exog.index = index.copy()
          data = data.set_index(names[index_idx])
  
      exog_name = list(exog.columns)
      dataset = Dataset(
          data=data,
          names=list(names),
          endog=endog,
          exog=exog,
          endog_name=endog_name,
          exog_name=exog_name,
      )
      return dataset
  
imports: from statsmodels.compat.python import lrange
  from io import StringIO
  from os import environ, makedirs
  from os.path import abspath, dirname, exists, expanduser, join
  import shutil
  from urllib.error import HTTPError, URLError
  from urllib.parse import urljoin
  from urllib.request import urlopen
  import numpy
  from pandas import Index, read_csv, read_stata
label_desc: Use image node_id 2 for calling a global function with example usage: process_pandas(data, endog_idx, exog_idx, index_idx) and returns: dataset